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Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. Historical air quality measurement data were gathered from various local sensors positioned at different sites in Skopje, along with data on meteorological conditions from publicly available APIs. Various implementations and hyperparameters of several deep learning models were compared. Additionally, an analysis was conducted to assess the influence of urban traffic on air and noise pollution, leveraging the COVID-19 lockdown periods when traffic was virtually non-existent. The outcomes suggest that the proposed models can effectively predict air pollution. From the urban traffic perspective, the findings indicate that car traffic is not the major contributing factor to air pollution., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kostadinov et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Marcos-Zambrano, López-Molina, Bakir-Gungor, Frohme, Karaduzovic-Hadziabdic, Klammsteiner, Ibrahimi, Lahti, Loncar Turukalo, Dhamo, Simeon, Nechyporenko, Pio, Przymus, Sampri, Trajkovik, Lacruz-Pleguezuelos, Aasmets, Araujo, Anagnostopoulos, Aydemir, Berland, Calle, Ceci, Duman, Gündoğdu, Havulinna, Kaka Bra, Kalluci, Karav, Lode, Lopes, May, Nap, Nedyalkova, Paciência, Pasic, Pujolassos, Shigdel, Susín, Thiele, Truică, Wilmes, Yilmaz, Yousef, Claesson, Truu, Carrillo de Santa Pau.)

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices., Competing Interests: CJ is employed by Biome diagnostics GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 D’Elia, Truu, Lahti, Berland, Papoutsoglou, Ceci, Zomer, Lopes, Ibrahimi, Gruca, Nechyporenko, Frohme, Klammsteiner, Pau, Marcos-Zambrano, Hron, Pio, Simeon, Suharoschi, Moreno-Indias, Temko, Nedyalkova, Apostol, Truică, Shigdel, Telalović, Bongcam-Rudloff, Przymus, Jordamović, Falquet, Tarazona, Sampri, Isola, Pérez-Serrano, Trajkovik, Klucar, Loncar-Turukalo, Havulinna, Jansen, Bertelsen and Claesson.)

Background: Overweight and obesity have been linked to several serious health problems and medical conditions. With more than a quarter of the young population having weight problems, the impacts of overweight and obesity on this age group are particularly critical. Mobile health (mHealth) apps that support and encourage positive health behaviors have the potential to achieve better health outcomes. These apps represent a unique opportunity for young people (age range 10-24 years), for whom mobile phones are an indispensable part of their everyday living. However, despite the potential of mHealth apps for improved engagement in health interventions, user adherence to these health interventions in the long term is low., Objective: The aims of this research were to (1) review and analyze mHealth apps targeting obesity and overweight and (2) propose guidelines for the inclusion of user interface design patterns (UIDPs) in the development of mHealth apps for obese young people that maximizes the impact and retention of behavior change techniques (BCTs)., Methods: A search for apps was conducted in Google Play Store using the following search string: ["best weight loss app for obese teens 2020"] OR ["obesity applications for teens"] OR ["popular weight loss applications"]. The most popular apps available in both Google Play and Apple App Store that fulfilled the requirements within the inclusion criteria were selected for further analysis. The designs of 17 mHealth apps were analyzed for the inclusion of BCTs supported by various UIDPs. Based on the results of the analysis, BCT-UI design guidelines were developed. The usability of the guidelines was presented using a prototype app., Results: The results of our analysis showed that only half of the BCTs are implemented in the reviewed apps, with a subset of those BCTs being supported by UIDPs. Based on these findings, we propose design guidelines that associate the BCTs with UIDPs. The focus of our guidelines is the implementation of BCTs using design patterns that are impactful for the young people demographics. The UIDPs are classified into 6 categories, with each BCT having one or more design patterns appropriate for its implementation. The applicability of the proposed guidelines is presented by mock-ups of the mHealth app "Morphe," intended for young people (age range 10-24 years). The presented use cases showcase the 5 main functionalities of Morphe: learn, challenge, statistics, social interaction, and settings., Conclusions: The app analysis results showed that the implementation of BCTs using UIDPs is underutilized. The purposed guidelines will help developers in designing mHealth apps for young people that are easy to use and support behavior change. Future steps involve the development and deployment of the Morphe app and the validation of its usability and effectiveness., (©Elena Vlahu-Gjorgievska, Andrea Burazor, Khin Than Win, Vladimir Trajkovik. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 19.01.2023.)

Background: The COVID-19 pandemic outbreak has led to a sudden change in education, closing schools and shifting to online teaching, which has become an enormous challenge for teachers and students. Implementing adequate online pedagogical approaches and integrating different digital tools in the teaching process have become a priority in educational systems. Finding a way to keep students' interest and persistence in learning is an important issue that online education is facing. One possible way to establish engaging and interactive learning environments, using the energy and enthusiasm of students for educational purposes, is the use of game-based learning activities and gamification of different parts of the educational process., Objective: This paper presents a use case of migrating an escape room-style educational game to an online environment by using the design thinking methodology. We wanted to show that the design thinking methodology is useful to create engaging and motivating online games that provide educational value., Methods: Starting from students' perspective, we created a simple digital escape room-style game where students got an opportunity to self-assess their knowledge in computer science at their own pace. Students tested this prototype game, and their opinions about the game were collected through an online survey. The test's goal was to evaluate the students' perceptions about the implemented digital escape room-style educational game and gather information about whether it could achieve students' engagement in learning computer science during online teaching., Results: In total, 117 students from sixth and seventh grades completed the survey regarding the achieved student engagement. Despite the differences in students' answers about game complexity and puzzle difficulty, most students liked the activity (mean 4.75, SD 0.67, on a scale from 1 to 5). They enjoyed the game, and they would like to participate in this kind of activity again (mean 4.74, SD 0.68). All (n=117, 100%) students found the digital escape room-style educational game interesting for playing and learning., Conclusions: The results confirmed that digital escape room-style games could be used as an educational tool to engage students in the learning process and achieve learning outcomes. Furthermore, the design thinking methodology proved to be a useful tool in the process of adding novel educational value to the digital escape room-style game., (©Maja Videnovik, Tone Vold, Georgina Dimova, Linda Vibeke Kiønig, Vladimir Trajkovik. Originally published in JMIR Serious Games (https://games.jmir.org), 26.09.2022.)

Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyzed in detail. Our analysis found that the Eight Hop Test measurements can be performed with motion, force, and imaging sensors.

Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.

Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach's feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.

Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers' well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.

Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease's progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients' sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Marcos-Zambrano, Karaduzovic-Hadziabdic, Loncar Turukalo, Przymus, Trajkovik, Aasmets, Berland, Gruca, Hasic, Hron, Klammsteiner, Kolev, Lahti, Lopes, Moreno, Naskinova, Org, Paciência, Papoutsoglou, Shigdel, Stres, Vilne, Yousef, Zdravevski, Tsamardinos, Carrillo de Santa Pau, Claesson, Moreno-Indias and Truu.)

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models' performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008-2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries' search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement.

Background: Latest achievement technologies allow engineers to develop medical systems that medical doctors in the health care system could not imagine years ago. The development of signal theory, intelligent systems, biophysics and extensive collaboration between science and technology researchers and medical professionals, open up the potential for preventive, real-time monitoring of patients. With the recent developments of new methods in medicine, it is also possible to predict the trends of the disease development as well the systemic support in diagnose setting. Within the framework of the needs to track the patient health parameters in the hospital environment or in the case of road accidents, the researchers had to integrate the knowledge and experiences of medical specialists in emergency medicine who have participated in the development of a mobile wireless monitoring system designed for real-time monitoring of victim vital parameters. Emergency medicine responders are first point of care for trauma victim providing prehospital care, including triage and treatment at the scene of incident and transport from the scene to the hospital. Continuous monitoring of life functions allows immediate detection of a deterioration in health status and helps out in carrying out principle of continuous e-triage. In this study, a mobile wireless monitoring system for measuring and recording the vital parameters of the patient was presented and evaluated. Based on the measured values, the system is able to make triage and assign treatment priority for the patient. The system also provides the opportunity to take a picture of the injury, mark the injured body parts, calculate Glasgow Coma Score, or insert/record the medication given to the patient. Evaluation of the system was made using the Technology Acceptance Model (TAM). In particular we measured: perceived usefulness, perceived ease of use, attitude, intention to use, patient status and environmental status., Methods: A functional prototype of a developed wireless sensor-based system was installed at the emergency medical (EM) department, and presented to the participants of this study. Thirty participants, paramedics and doctors from the emergency department participated in the study. Two scenarios common for the prehospital emergency routines were considered for the evaluation. Participants were asked to answer the questions referred to these scenarios by rating each of the items on a 5-point Likert scale., Results: Path coefficients between each measured variable were calculated. All coefficients were positive, but the statistically significant were only the following: patient status and perceive usefulness (β = 0.284, t = 2.097), environment (both urban a nd rural) and perceive usefulness (β = 0.247, t = 2.570; β = 0.329, t = 2.083, respectively), and perceive usefulness and behavioral intention (β = 0.621 t = 7.269). The variance of intention is 47.9%., Conclusions: The study results show that the proposed system is well accepted by the EM personnel and can be used as a complementary system in EM department for continuous monitoring of patients' vital signs.

Demographic data suggest a rapid aging trend in the active workforce. The concept of aging at work comes from the urgent requirement to help the aging workforce of the contemporary industries to maintain productivity while achieving a work and private life balance. While there is plenty of research focusing on the aging population, current research activities on policies covering the concept of aging at work are limited and conceptually different. This paper aims to review publications on aging at work, which could lead to the creation of a framework that targets governmental decision-makers, the non-governmental sector, the private sector, and all of those who are responsible for the formulation of policies on aging at work. In August 2019 we searched for peer-reviewed articles in English that were indexed in PubMed, IEEE Xplore, and Springer and published between 2008 and 2019. The keywords included the following phrases: "successful aging at work", "active aging at work", "healthy aging at work", "productive aging at work", and "older adults at work". A total of 47,330 publications were found through database searching, and 25,187 publications were screened. Afterwards, 7756 screened publications were excluded from the further analysis, and a total of 17,431 article abstracts were evaluated for inclusion. Finally, further qualitative analysis included 1375 articles, of which about 24 are discussed in this article. The most prominent works suggest policies that encourage life-long learning, and a workforce that comprises both younger and older workers, as well as gradual retirement.

The free movement of European citizens across member states of the European Union adds an important level of complexity to strategic efforts of health interoperability. The use of electronic health data has been marked as an important strategic activity and policy to improve healthcare in European countries. Cross-border healthcare depends on the ability to set up shared practices with respect to patient data exchange across the countries. Data flow must comply with demanding security, legal and interoperability requirements, as defined by the European Patients Smart Open Services project specifications. The aim of this article is to propose a novel design of healthcare data warehouse based on the restructured Extract-Transform-Load process. We describe a portal framework that offers a comprehensive set of interoperability services to enable national e-Health platforms to set up cross-border health information networks compliant with European Patients Smart Open Services. The presented approach incorporates the technical and organizational interoperability by interconnecting Health Level Seven standard and Open National Contact Points framework in order to provide a modular, scalable and inter-operating architecture.

Background: Connected health (CH) technologies have resulted in a paradigm shift, moving health care steadily toward a more patient-centered delivery approach. CH requires a broad range of disciplinary expertise from across the spectrum to work in a cohesive and productive way. Building this interdisciplinary relationship at an earlier stage of career development may nurture and accelerate the CH developments and innovations required for future health care., Objective: This study aimed to explore the perceptions of interdisciplinary CH researchers regarding the design and delivery of an interdisciplinary education (IDE) module for disciplines currently engaged in CH research (engineers, computer scientists, health care practitioners, and policy makers). This study also investigated whether this module should be delivered as a taught component of an undergraduate, master's, or doctoral program to facilitate the development of interdisciplinary learning., Methods: A qualitative, cross-institutional, multistage research approach was adopted, which involved a background study of fundamental concepts, individual interviews with CH researchers in Greece (n=9), and two structured group feedback sessions with CH researchers in Ireland (n=10/16). Thematic analysis was used to identify the themes emerging from the interviews and structured group feedback sessions., Results: A total of two sets of findings emerged from the data. In the first instance, challenges to interdisciplinary work were identified, including communication challenges, divergent awareness of state-of-the-art CH technologies across disciplines, and cultural resistance to interdisciplinarity. The second set of findings were related to the design for interdisciplinarity. In this regard, the need to link research and education with real-world practice emerged as a key design concern. Positioning within the program context was also considered to be important with a need to balance early intervention to embed integration with later repeat interventions that maximize opportunities to share skills and experiences., Conclusions: The authors raise and address challenges to interdisciplinary program design for CH based on an abductive approach combining interdisciplinary and interprofessional education literature and the collection of qualitative data. This recipe approach for interdisciplinary design offers guidelines for policy makers, educators, and innovators in the CH space. Gaining insight from CH researchers regarding the development of an IDE module has offered the designers a novel insight regarding the curriculum, timing, delivery, and potential challenges that may be encountered., (©Ioanna Chouvarda, Nicola Mountford, Vladimir Trajkovik, Tatjana Loncar-Turukalo, Tara Cusack. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 13.11.2019.)

Background: Wearable sensing and information and communication technologies are key enablers driving the transformation of health care delivery toward a new model of connected health (CH) care. The advances in wearable technologies in the last decade are evidenced in a plethora of original articles, patent documentation, and focused systematic reviews. Although technological innovations continuously respond to emerging challenges and technology availability further supports the evolution of CH solutions, the widespread adoption of wearables remains hindered., Objective: This study aimed to scope the scientific literature in the field of pervasive wearable health monitoring in the time interval from January 2010 to February 2019 with respect to four important pillars: technology, safety and security, prescriptive insight, and user-related concerns. The purpose of this study was multifold: identification of (1) trends and milestones that have driven research in wearable technology in the last decade, (2) concerns and barriers from technology and user perspective, and (3) trends in the research literature addressing these issues., Methods: This study followed the scoping review methodology to identify and process the available literature. As the scope surpasses the possibilities of manual search, we relied on the natural language processing tool kit to ensure an efficient and exhaustive search of the literature corpus in three large digital libraries: Institute of Electrical and Electronics Engineers, PubMed, and Springer. The search was based on the keywords and properties to be found in articles using the search engines of the digital libraries., Results: The annual number of publications in all segments of research on wearable technology shows an increasing trend from 2010 to February 2019. The technology-related topics dominated in the number of contributions, followed by research on information delivery, safety, and security, whereas user-related concerns were the topic least addressed. The literature corpus evidences milestones in sensor technology (miniaturization and placement), communication architectures and fifth generation (5G) cellular network technology, data analytics, and evolution of cloud and edge computing architectures. The research lag in battery technology makes energy efficiency a relevant consideration in the design of both sensors and network architectures with computational offloading. The most addressed user-related concerns were (technology) acceptance and privacy, whereas research gaps indicate that more efforts should be invested into formalizing clear use cases with timely and valuable feedback and prescriptive recommendations., Conclusions: This study confirms that applications of wearable technology in the CH domain are becoming mature and established as a scientific domain. The current research should bring progress to sustainable delivery of valuable recommendations, enforcement of privacy by design, energy-efficient pervasive sensing, seamless monitoring, and low-latency 5G communications. To complement technology achievements, future work involving all stakeholders providing research evidence on improved care pathways and cost-effectiveness of the CH model is needed., (©Tatjana Loncar-Turukalo, Eftim Zdravevski, José Machado da Silva, Ioanna Chouvarda, Vladimir Trajkovik. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.09.2019.)

Background: Connected health (CH), as a new paradigm, manages individual and community health in a holistic manner by leveraging a variety of technologies and has the potential for the incorporation of telehealth and integrated care services, covering the whole spectrum of health-related services addressing healthy subjects and chronic patients. The reorganization of services around the person or citizen has been expected to bring high impact in the health care domain. There are a series of concerns (eg, contextual factors influencing the impact of care models, the cost savings associated with CH solutions, and the sustainability of the CH ecosystem) that should be better addressed for CH technologies to reach stakeholders more successfully. Overall, there is a need to effectively establish an understanding of the concepts of CH impact. As services based on CH technologies go beyond standard clinical interventions and assessments of medical devices or medical treatments, the need for standardization and for new ways of measurements and assessments emerges when studying CH impact., Objective: This study aimed to introduce the CH impact framework (CHIF) that serves as an approach to assess the impact of CH services., Methods: This study focused on the subset of CH comprising services that directly address patients and citizens on the management of disease or health and wellness. The CHIF was developed through a multistep procedure and various activities. These included, as initial steps, a literature review and workshop focusing on knowledge elicitation around CH concepts. Then followed the development of the initial version of the framework, refining of the framework with the experts as a result of the second workshop, and, finally, composition and deployment of a questionnaire for preliminary feedback from early-stage researchers in the relevant domains., Results: The framework contributes to a better understanding of what is CH impact and analyzes the factors toward achieving it. CHIF elaborates on how to assess impact in CH services. These aspects can contribute to an impact-aware design of CH services. It can also contribute to a comparison of CH services and further knowledge of the domain. The CHIF is based on 4 concepts, including CH system and service outline, CH system end users, CH outcomes, and factors toward achieving CH impact. The framework is visualized as an ontological model., Conclusions: The CHIF is an initial step toward identifying methodologies to objectively measure CH impact while recognizing its multiple dimensions and scales., (©Ioanna Chouvarda, Christos Maramis, Kristina Livitckaia, Vladimir Trajkovik, Serhat Burmaoglu, Hrvoje Belani, Jan Kool, Roman Lewandowski, The ENJECT Working Group 1 Network. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.09.2019.)

The emerging demographic trends toward an aging population, demand new ways and solutions to improve the quality of elderly life. These include, prolonged independent living, improved health care, and reduced social isolation. Recent technological advances in the field of assistive robotics bring higher sophistication and various assistive abilities that can help in achieving these goals. In this paper, we present design and validation of a low-cost telepresence robot that can assist the elderly and their professional caregivers, in everyday activities. The developed robot structure and its control objectives were tested in, both, a simulation and experimental environment. On-field experiments were done in a private elderly care center involving elderly persons and caregivers as participants. The goal of the evaluation study was to test the software architecture and the robot capabilities for navigation, as well as the robot manipulator. Moreover, participants' reactions toward a possible adoption of the developed robot system in everyday activities were assessed. The obtained results of the conducted evaluation study are also presented and discussed.

This study promotes a novel teaching approach for integration of children's traditional games in elementary school program. It gives description of six traditional games and their educational prospects, implemented in six learning sessions in five elementary schools in Macedonia, involving 102 students. The comparison of learning achievements between these learning sessions and standard classes revealed increased students' learning performance on comparable topics. To understand the reason for improvement, we have surveyed students after each session and tested the gathered data set via the development of a structural equation model that examines the relationships between student's personality traits, motivation and experience with learning outcomes. The findings show that students' achievements were directly influenced by students' intrinsic and extrinsic motivational factors, as well as perceived experience. Additionally, the integration of traditional games in the elementary school classroom was equally accepted among all students, since their personality traits did not directly influence their experience or learning outcomes. Still, the link between the students' personality dimensions and motivation revealed that introvert children might have slightly increased motivation and possibility to open up during game-play in such collaborative environments., Competing Interests: The authors have declared that no competing interests exist.

Low-cost biosensors combined with low-cost portable devices can be very useful in time critical situations of mass casualties, when fast triage procedure must be attained. A methodology that uses ECG to derive the vital parameters (heart rate and respiratory rate) needed for the triage procedure is presented and it is aimed to leverage affordable low-cost equipment that can be easily utilized by urgent medical units or even volunteers in events of considerable number of injured civilians. The methodology relies on selected well-known and published algorithms for heart rate and respiratory rate derivation from a given ECG signal. It consists of methods for R-wave detection, kurtosis computation, smoothing, and finding peaks. The proposed approach is shown to offer a good trade-off between the accurate measurement of the parameters and their fast derivation. It has been evaluated by using a publicly available database. Its robustness is measured in terms of accuracy estimation, showing a sensitivity of 0.87 for heart rate and 0.74 for respiratory rate, a sensitivity of 0.76 considering the triage process and an average-case execution time of 0.02 seconds, making it suitable for real-time applications.

In the nineties, numerous studies began to highlight the problem of the increasing number of people with Alzheimer's disease in developed countries, especially in the context of demographic progress. At the same time, the 21st century is typical of the development of advanced technologies that penetrate all areas of human life. Digital devices, sensors, and intelligent applications are tools that can help seniors and allow better communication and control of their caregivers. The aim of the paper is to provide an up-to-date summary of the use of technological solutions for improving health and safety for people with Alzheimer's disease. Firstly, the problems and needs of senior citizens with Alzheimer's disease (AD) and their caregivers are specified. Secondly, a scoping review is performed regarding the technological solutions suggested to assist this specific group of patients. Works obtained from the following libraries are used in this scoping review: Web of Science, PubMed, Springer, ACM and IEEE Xplore. Four independent reviewers screened the identified records and selected relevant articles which were published in the period from 2007 to 2018. A total of 6,705 publications were selected. In all, 128 full papers were screened. Results obtained from the relevant studies were furthermore divided into the following categories according to the type and use of technologies: devices, processing, and activity recognition. The leading technological solution in the category of devices are wearables and ambient noninvasive sensors. The introduction and utilization of these technologies, however, bring about challenges in acceptability, durability, ease of use, communication, and power requirements. Furthermore, it needs to be pointed out that these technological solutions should be based on open standards., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Background and Objectives: Categorization of the casualties in accordance with medical care priorities is crucial in a military environment. Automation of the triage process is still a challenging task. The goal of the paper is to propose a novel algorithm for automation of medical emergency protocol in the military environment by the creation of classifiers that can provide accurate prioritization of injured soldier cases. It is a part of a complex military telemedicine system that provides continuous monitoring of soldiers' vital data gathered on-site using an unobtrusive set of sensors., Methods: After pre-processing the collected raw physiological data and eliminating the outliers using Naïve Bayesian Classifier, the system is capable of calculating the risk level and categorizing the victims based on Markov Decision Process. The NBC has been trained with a dataset that has contained labels and 6 features. Training set has held 8000 randomly chosen samples. Twenty percent of the determined dataset has been used for the validation set., Results: For algorithm verification, several evaluation scenarios have been created. In each scenario, randomly generated vital sign data describing the hypothetical health condition of soldiers was contemporarily assessed by the system as well as by 50 experienced military medical physicians., Conclusion: The obtained correlation result of the proposed algorithm and medical physicians' classifications is strong evidence that the system can be implemented in warfare emergency medicine.

Novel information and communication technologies create possibilities to change the future of health care. Ambient Assisted Living (AAL) is seen as a promising supplement of the current care models. The main goal of AAL solutions is to apply ambient intelligence technologies to enable elderly people to continue to live in their preferred environments. Applying trained models from health data is challenging because the personalized environments could differ significantly than the ones which provided training data. This paper investigates the effects on activity recognition accuracy using single accelerometer of personalized models compared to models built on general population. In addition, we propose a collaborative filtering based approach which provides balance between fully personalized models and generic models. The results show that the accuracy could be improved to 95% with fully personalized models, and up to 91.6% with collaborative filtering based models, which is significantly better than common models that exhibit accuracy of 85.1%. The collaborative filtering approach seems to provide highly personalized models with substantial accuracy, while overcoming the cold start problem that is common for fully personalized models.

Objective: The aim of the study was to identify primiparous pregnant women with a higher risk for obstetric anal sphincter injuries (OASIS) based on obstetric characteristics (risk factors)., Study Design: In the retrospective case control study primiparous women were examined using endoanal ultrasonography (EUS) for OASIS identification 6-12 weeks after delivery. Obstetric characteristics for OASIS were collected from the mothers' medical records. The univariate analysis of maternal (age at delivery, maternal height, weight, BMI), infant (length, weight and head circumference) and birth (pregnancy duration, labour and delivery duration, episiotomy, vacuum extraction and oxytocin augmentation) risk factors, Pearson correlations and information gain were carried out. The cut-off values for the aforementioned risk factors divided the patients into groups with higher and lower risk of OASIS., Results: The data of 84 primiparous women with OASIS, and 58 without, were analysed. Those newborns born to women in the OASIS group were heavier (P<0.05), with the cut-off at 3420g (72% probability of OASIS), had a larger head circumference (P<0.001), cut-off at 36cm (84% probability of OASIS), and were longer (P<0.05), cut-off at 50.5cm (74% probability of OASIS). The maternal age and body mass index (BMI) were risk factors for OASIS (P<0.05 and P<0.05, respectively) with a probability of 83% in women younger than 27.5 years and a 78% probability if BMI was higher than 28kg/m 2 . The incidence of OASIS was not higher in women with episiotomy or vacuum extraction, but it was higher in oxytocin augmentation (P<0.031)., Conclusion: The findings can assist in identification of pregnant women with a higher risk of OASIS who require special attention at delivery to prevent it. In high risk women EUS is indicated to identify and treat possible OASIS as early as possible in order to prevent anal incontinence., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Purpose - Information systems play a significant role in the improving of health and healthcare, as well as in the planning and financing of health services. Fund's Information System is an essential component of the information infrastructure that allows assessment of the impact of changes in health insurance and healthcare for the population. The purpose of this paper is to give a brief overview of the affection of e-services and electronic data exchange (between Fund's information systems and other IT systems) at the quality of service for insured people and savings funds. Design/methodology/approach - The authors opted for an exploratory study using the e-services implemented in Health Insurance Fund (HIF) of Macedonia and data which were complemented by documentary analysis, including brand documents and descriptions of internal processes. In this paper is presented an analysis of the financial aspects of some e-services in HIF of Macedonia by using computer-based information systems and calculating the financial implications on insured people, companies and healthcare providers. Findings - The analysis conducted in this paper shows that the HIF's e-services would have a positive impact for the insured people, healthcare providers and companies when fulfilling their administrative obligations and exercising their rights. Originality/value - The analysis presented in this paper can serve as a valuable input for the healthcare authorities in making decisions related to introducing e-services in healthcare. These enhanced e-services will improve the quality service of the HIF.

Nowadays, there is a growing interest towards the adoption of novel ICT technologies in the field of medical monitoring and personal health care systems. This paper proposes design of a connected health algorithm inspired from social computing paradigm. The purpose of the algorithm is to give a recommendation for performing a specific activity that will improve user's health, based on his health condition and set of knowledge derived from the history of the user and users with similar attitudes to him. The algorithm could help users to have bigger confidence in choosing their physical activities that will improve their health. The proposed algorithm has been experimentally validated using real data collected from a community of 1000 active users. The results showed that the recommended physical activity, contributed towards weight loss of at least 0.5 kg, is found in the first half of the ordered list of recommendations, generated by the algorithm, with the probability > 0.6 with 1 % level of significance.

The Critical Home Care System - CHCS, we propose, achieves permanent advising, frequent control appointments and quick reaction to critical conditions by constant remote monitoring of patient's vital signs from the hospital, while staying at his home. Physicians react properly to the developing condition, contacting the patient or a member of the household, or sending an ambulance in an emergency. The CHCS additionally provides constant inspection of the patient's condition to the ambulance doctor in emergency situations and to the urgent centre staff to prepare better for accepting the patient, enabling a fully connected emergency intervention. In this paper we will concentrate on the data flow during the emergency intervention in this highly collaborative system.